Interaction between Attention and Bottom-Up Saliency
Mediates the Representation of Foreground and
Background in an Auditory Scene
Mounya Elhilali1., Juanjuan Xiang2., Shihab A. Shamma3,4, Jonathan Z. Simon3,5*
1 Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America, 2 Starkey Laboratories, Eden Prairie,
Minnesota, United States of America, 3 Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America,
4 Institute for Systems Research, University of Maryland, College Park, Maryland, United States of America, 5 Department of Biology, University of Maryland, College Park,
Maryland, United States of America
Abstract
The mechanism by which a complex auditory scene is parsed into coherent objects depends on poorly understood
interactions between task-driven and stimulus-driven attentional processes. We illuminate these interactions in a
simultaneous behavioral–neurophysiological study in which we manipulate participants’ attention to different features of
an auditory scene (with a regular target embedded in an irregular background). Our experimental results reveal that
attention to the target, rather than to the background, correlates with a sustained (steady-state) increase in the measured
neural target representation over the entire stimulus sequence, beyond auditory attention’s well-known transient effects on
onset responses. This enhancement, in both power and phase coherence, occurs exclusively at the frequency of the target
rhythm, and is only revealed when contrasting two attentional states that direct participants’ focus to different features of
the acoustic stimulus. The enhancement originates in auditory cortex and covaries with both behavioral task and the
bottom-up saliency of the target. Furthermore, the target’s perceptual detectability improves over time, correlating
strongly, within participants, with the target representation’s neural buildup. These results have substantial implications for
models of foreground/background organization, supporting a role of neuronal temporal synchrony in mediating auditory
object formation.
Citation: Elhilali M, Xiang J, Shamma SA, Simon JZ (2009) Interaction between Attention and Bottom-Up Saliency Mediates the Representation of Foreground
and Background in an Auditory Scene. PLoS Biol 7(6): e1000129. doi:10.1371/journal.pbio.1000129
Academic Editor: Timothy D. Griffiths, Newcastle University Medical School, United Kingdom
Received December 15, 2008; Accepted May 5, 2009; Published June 16, 2009
Copyright: ß 2009 Elhilali et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Support has been provided by National Institutes of Health (NIH) grants R01DC008342, 1R01DC007657, and (via the Collaborative Research in
Computational Neuroscience National Science Foundation/NIH joint mechanism) 1R01AG027573. The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
Abbreviations: MEG, magnetoencephalography.
* E-mail: jzsimon@umd.edu
. These authors contributed equally to this work.
target sounds from a background of distracters [20–28]. It is
largely unknown how top-down (e.g., task-driven or contextdependent) and bottom-up (e.g., acoustic saliency or ‘‘pop-out’’)
attentional processes interact to parse a complex auditory scene
[29,30].
In a simultaneous behavioral and neurophysiological study
using magnetoencephalography (MEG), we illuminate this interaction using stimuli shown in Figure 1A, consisting of a repeating
target note in the midst of random interferers (‘‘maskers’’). This
design generalizes paradigms commonly used in informational
masking experiments, [31], which explore how listeners’ ability to
perceive an otherwise salient auditory element is strongly affected
by the presence of competing elements. For these stimuli, the
ability to segregate the target note depends on various acoustic
parameters, including the width of the spectral protection region
(the spectral separation between target and masker frequencies).
We adapt classic informational masking stimuli to the purposes of
this study by randomly desynchronizing all background maskers
throughout the duration of the trial, making the target note the
Introduction
Attention is the cognitive process underlying our ability to focus
on specific components of the environment while ignoring others.
By its very definition, attention plays a key role in defining what
foreground is, i.e., an object of attention, and differentiating it
from task-irrelevant clutter, or background [1–5]. In the visual
modality, studies have shown that figure/ground segmentation is
mediated by a competition for neural resources between objects in
the scene [2,6,7]. This competition is biased in favor of different
objects via top-down attention as well as behavioral and contextual
effects that work to complement or counteract automatic bottomup processes. An intricate neural circuitry has been postulated to
take place in this process spanning primary visual, extrastriate,
temporal, and frontal cortical areas [7–19].
In the auditory modality, however, there have been a limited
number of studies that attempted to explore the neural
underpinnings of attention in the context of auditory stream
segregation, and the mechanisms governing the extraction of
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compared to the target task, reflects a different top-down bias in
the way the same stimulus is parsed. The behavioral performance
was unchanged whether tested under purely psychoacoustic or
neural recording conditions (no significant difference; unpaired ttest; target task: t = 20.75, p = 0.46; masker task: t = 0.09, p = 0.93).
For the neural recordings, we used the stimuli with the eightsemitones spectral protection region because they roughly
matched the behavioral performance across tasks (d-prime for
both is approximately equal to three). The target task is not at
ceiling with the chosen protection region, hence still engaging
participants’ selective attentional processes.
Depending on listeners’ attentional focus, the percept of an
auditory target in a complex scene is differentially mirrored by the
responses of neurons in auditory cortex. Using the high temporal
resolution of MEG, we measure the neural responses to this
stimulus paradigm in 14 human participants. Figure 2A reveals
that, during the performance of the target task, the target rhythm
emerges as a strong 4-Hz component in the neural signal of an
individual participant. In contrast, during the masker task, the
cortical response entrained at 4 Hz is noticeably suppressed in
comparison (Figure 2A, right panel). This differential activation is
strong evidence of the modulatory effect of task-dependent
attention on the neural representation of a single acoustic stimulus,
much like visual attention [39,40]. Additionally, this attentional
effect on the neural signal is not just momentary but is sustained
over the duration of the trial (steady state).
This attentional effect is confirmed in the population of 14
participants (Figure 2B), with an average normalized neural
response of 20.9 in the target task and 8.3 in the masker task: a
gain of more than two and a half for neural phase-locked,
sustained activity when participants’ attention is directed towards
the repeating note (individually, 11 out of 14 participants showed a
significant increase: paired t-test, p,1024). Direct correlation
between the target task neural response and target task behavior is
not observed, but as shown below, changes in a participant’s target
neural response are significantly correlated with changes in the
participant’s behavioral responses.
The MEG magnetic field distributions of the target rhythm
response component, examples of which are shown in the inset of
the graphs in Figure 2A, reveal the stereotypical pattern for neural
activity originating separately in left and right auditory cortex. The
neural sources of all the target rhythm response components with a
sufficiently high signal-to-noise ratio originate in auditory cortex
[41]. The neural source’s mean displacement from the source of
the auditory M100 response [42] was significantly different (twotailed t-test; t = 2.9, p = 0.017) by 13.864.9 mm in the anterior
direction, for the left auditory cortex only (no significant
differences were found in the right hemisphere due to higher
variability there). The displacement was not statistically significant
in the remaining directions (3.263.5 mm lateral; 11.366.4 mm
superior); the goodness of fit for these sources was 0.5160.05
(artificially reduced in accordance with [43]). Assuming an M100
origin of planum temporale, an area of associative auditory cortex,
this is consistent with an origin for the neural response to the target
rhythm in Heschl’s gyrus, the site of core auditory cortex including
primary auditory cortex, and a region known to phase-lock well to
4-Hz rhythms [44].
The neural response change at the target rate of 4 Hz is highly
significant (bootstrap across participants, p,1024) (Figure 3A). In
contrast, there is no significant change in normalized neural
response at other frequencies, whether at frequencies nearby (one
frequency bin on either side of 4 Hz) or distant (alpha, theta, and
low gamma band frequencies sampled with approximately 5-Hz
spacing up to 55 Hz). This demonstrates that this feature-selective
Author Summary
Attention is the cognitive process underlying our ability to
focus on specific aspects of our environment while
ignoring others. By its very definition, attention plays a
key role in differentiating foreground (the object of
attention) from unattended clutter, or background. We
investigate the neural basis of this phenomenon by
engaging listeners to attend to different components of
a complex acoustic scene. We present a spectrally and
dynamically rich, but highly controlled, stimulus while
participants perform two complementary tasks: to attend
either to a repeating target note in the midst of random
interferers (‘‘maskers’’), or to the background maskers
themselves. Simultaneously, the participants’ neural responses are recorded using the technique of magnetoencephalography (MEG). We hold all physical parameters of
the stimulus fixed across the two tasks while manipulating
one free parameter: the attentional state of listeners. The
experimental findings reveal that auditory attention
strongly modulates the sustained neural representation
of the target signals in the direction of boosting
foreground perception, much like known effects of visual
attention. This enhancement originates in auditory cortex,
and occurs exclusively at the frequency of the target
rhythm. The results show a strong interaction between the
neural representation of the attended target with the
behavioral task demands, the bottom-up saliency of the
target, and its perceptual detectability over time.
only regular frequency channel in the sequence (with repetition
rhythm of 4 Hz). The informational masking paradigm has been
shown to invoke similar mechanisms to those at play in classic
stream segregation experiments [32,33], both in the systematic
dependence of performance on the size of masker–target spectral
separation, as well as the improvement of performance over time
over the course of few seconds.
While maintaining the same physical stimulus, we contrasted
the performance of human listeners in two complementary tasks:
(1) a ‘‘target task’’ in which participants are asked to detect a
frequency-shifted (DF) deviant in the repeating target signal; and
(2) a ‘‘masker task’’ in which participants are asked to detect a
sudden temporal elongation (DT) of the masker notes. Crucially,
attention is required to perform either task, but the participants’
attention must be focused on different sound components of the
acoustic stimulus in each case. Additionally, all the stimuli are
diotic (identical in both ears), averting any confounding effects of
spatial attention.
Results
The effect of spectral protection region width on the
performance of both tasks is illustrated in Figure 1B. In the left
panel, it can be seen that the detectability of the target becomes
easier with increasing protection region (significantly positive
slope; bootstrap across participants, p,1024), a result that is in line
with previous hypotheses of streaming that correlate the ease of
target detection with the frequency selectivity of neurons in the
central auditory system [34–38].
In contrast, the same manipulations of protection region do not
substantively affect masker task performance (right panel) (not
significantly different from zero; bootstrap across participants,
p.0.3). The masker task, designed to divert attentional resources
away from the target, involves a more diffuse attention to the
spectrally broad and distributed masker configuration; and
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Figure 1. Stimulus description and behavioral performance. (A) Cartoon spectrogram of a typical stimulus. The stimulus consists of a
repeating target note embedded in random interferers. A spectral protection region surrounds the target frequency with a spectral width of twice
the minimal distance between the target note and nearest masker component (orange band). In the target task, participants were instructed to
detect a frequency-shifted (DF) deviant in the repeating target notes. In the masker task, participants were instructed to detect a sudden temporal
elongation (DT) of the masker notes. (B) Behavioral performance results for target and masker tasks, as measured by d-prime as a function of spectral
protection region width. Orange (respectively, light-blue) lines show the mean performance in task detection in the target task (respectively, masker
task) in the psychoacoustical study. Red (respectively, dark-blue) points show the mean performance in task detection in the target task (respectively,
masker task) in the MEG study (eight-semitone condition only). Error bars represent standard error.
doi:10.1371/journal.pbio.1000129.g001
sustained attention modulates the cortical representation of the
specific feature, but not general intrinsic rhythms, whether in the
same band or other bands.
Changes in response phase coherence across channels were also
assessed at the same frequencies (Figure 3B, sample participant in
Figure 3C). This analysis focuses on the distant channel pairs with
enhanced phase coherence at each specific frequency. Only the
phase coherence at the target rate shows a significant enhancement
(bootstrap across participants, p = 0.002), further demonstrating that
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change from one form of attention to another does not modulate
general intrinsic rhythms. This 30% enhancement is distributed
across channel pairs, revealing increased phase coherence both
within and across hemispheres.
We also observe a task-dependent hemispheric asymmetry in
the representation of the neural response at the target rate. During
the target task, the left hemisphere showed a greater normalized
neural response than the right hemisphere (bootstrap across
participants, p = 0.001); during the masker task, the right
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Figure 2. Neural responses. (A) Power spectral density of MEG responses for a single participant (participant 14 in Figure 2B below) in target (left)
and masker (right) tasks, averaged over 20 channels. Insets: the MEG magnetic field distributions of the target rhythm response component. Red and
green contours represent the target magnetic field strength projected onto a line with constant phase. (B) Normalized neural response to the target
rhythm by participant (individual bars) and task (red for target task, blue for masker task). The normalized neural response is computed as the ratio of
the neural response power at the target rate (4 Hz) to the average power of the background neural activity (from 3–5 Hz; see Materials and Methods).
Bar height is the mean of the 20 best channels; error bars represent standard error. Light-pink background (respectively, light-blue) is the mean over
participants for the target task (respectively, masker task).
doi:10.1371/journal.pbio.1000129.g002
note range 250–500 Hz [45], because our stimuli were normalized
according to their spectral power, not loudness. We exploit this
physical sensitivity of the auditory system and determine the effect
of this target pop-out on the neural and behavioral performances
in both target and masker tasks. Figure 4A (orange line) confirms
that behavioral performance in the target task is easier for higherfrequency targets (.350 Hz) than for lower frequencies (t-test;
t = 23.3, p = 0.002). Correlated with this trend is an increased
neural response to the target for higher frequencies compared to
lower frequencies (red line) (increase not statistically significant
alone). Conversely, the masker task shows a trend of being
hemisphere showed a greater normalized neural response than the
left hemisphere (bootstrap across participants, p = 0.04) (Figure 3D).
Together with the behavioral demands of the task, the bottomup saliency of a target note contributes to both the neural response
and participant performance. A close examination of the physical
parameters of the stimulus reveals that the frequency of the target
note affects the audibility of the repeating rhythm, with higherfrequency targets popping out more prominently than their lowerfrequency counterparts. This variation in the pop-out sensation
may be explained by the contours of constant loudness of human
hearing showing an approximately 5-dB increase over the target
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Figure 3. Power and phase enhancement during target task. (A)
Normalized neural response of target task relative to masker task shows
differential enhancement exclusively at 4 Hz (the frequency of the
target rhythm). Each data point represents the difference between
normalized neural response of target relative to masker task; error bars
represent standard error The asterisk at 4 Hz shows that only that
particular frequency yields a statistically significant enhancement. (B)
Phase coherence between distant MEG channels of target relative to
masker task. The difference between the number of long-range channel
pairs with robust increased coherence in target task, and channel pairs
with decreased coherence, is normalized over the total number of longrange channel pairs. The phase enhancement is significant (shown with
asterisk) only at 4 Hz. (C) Channel pairs with robust coherence
difference at target rate for single participant, overlaid on the contour
map of normalized neural response at target rate. Each channel pair
with enhancement coherence is connected by a red line, whereas pairs
with decreased coherence are connected by a blue line. Coherence is
only analyzed for the 20 channels with the best normalized response to
target rhythm. (D) Neural responses to target across hemispheres. The
20 channels with the strongest normalized neural response at target
rate were chosen from the left and right hemispheres, respectively, to
represent the overall neural activity of each hemisphere. Neural
responses were averaged across the 20 channels, and 14 participants
were compared across hemispheres and tasks. The left hemisphere
shows stronger differential activation at target rate in target task,
whereas the right hemisphere shows stronger activation in masker task
(asterisks indicate that the differences are significant).
doi:10.1371/journal.pbio.1000129.g003
oppositely affected by the physical saliency of the target note despite
its irrelevance for task performance (approaching significance; t-test,
t = 1.8, p = 0.08). On the one hand, the neural power is increased for
high-frequency targets reflecting their increased audibility (dark-blue
line) (though not statistically significant alone). On the other hand, as
the target becomes more prominent, the participants’ performance
of the background task deteriorates, indicating a distraction effect
caused by the presence of the repeating note (light-blue line).
Additionally, phase coherence is significantly enhanced for highfrequency targets over low-frequency targets only during the target
task (bootstrap across participants, p,1023) (Figure 4C). This result
confirms that the physical parameters and acoustic saliency of a
signal can interfere with the intended attentional spotlight of listeners
and effectively deteriorate task performance [46,47], both neurally
and behaviorally.
In order to establish the correspondence within participants
between the neural and behavioral responses under both task
conditions in a parametric way, we quantified the slope (converted
into an angle) relating the normalized neural signal with the
listener’s d-prime performance on a per-participant basis. The
average slope angle for the target task is 55.1u, i.e., a positive slope,
demonstrating the positive correlation between the two measures.
Bootstrap analysis confirms this; Figure 4B, left panel, illustrates
both the bootstrap mean of 55.3u (green line) and the 5th to 95th
percentile confidence limits (gray background), all with positive
slopes. Analysis of the masker task also demonstrates the
anticorrelation trend between the neural and behavioral data,
with an average slope angle of 236.3u shown in yellow. The
bootstrap analysis also confirms this; Figure 4B (right panel) shows
that the 5th to 95th confidence intervals (gray background) yield a
robust negative slope with a bootstrap mean of 237.6u (green line).
The perceptual detectability of the regular target rhythm
improves over time, following a pattern that is highly correlated
with the neural buildup of the signal representation. Consistent
with previous findings of buildup of auditory stream segregation
[24,35,48,49], participants’ performance during the target task
improves significantly over several seconds as shown in Figure 5A
(solid orange line) (bootstrap across participants, p,10–4). This
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Figure 4. The effect of bottom-up acoustic saliency on behavior and neural responses. (A) Normalized neural response to target rhythm,
and behavioral performance, as a function of target frequency in target task (left) and masker task (right), averaged over participants. Error bars
represent standard error. (B) Correlation of behavioral and neural responses as a function of target frequency. The ratio of the neural to behavioral
response differences as a function of target frequency, interpreted as a slope angle, is averaged across participants yielding a mean slope angle of
55.1u for target (left) task and 236.3u for masker (right) task (yellow line). Bootstrap estimates (overlying green lines) and their 95% confidence
intervals (gray background) confirm the positive (respectively, negative) correlations for target (respectively, masker) task. (C) Phase coherence
between distant MEG channels of target relative to masker task for high-frequency targets over low-frequency targets. High- versus low-frequency
targets show significant enhancement only for target task (indicated by the asterisk).
doi:10.1371/journal.pbio.1000129.g004
Figure 5. Buildup over time of behavioral and neural responses in target task. (A) Normalized neural response to target rhythm, and
behavioral performance, as a function of time in target task, averaged over participants. Error bars represent standard error. Insets: the MEG magnetic
field distributions of the target rhythm response component for a single participant at representative moments in time (participant 10 from
Figure 2B). (B) Correlation of behavioral and neural responses as a function of time. The ratio of the neural to behavioral response trends as a function
of time, interpreted as a slope angle, is averaged across participants, yielding a mean slope angle of 34.3u (yellow line). Bootstrap estimates (overlying
green line) and the 95% confidence intervals (gray background) confirm the positive correlation between the psychometric and neurometric buildup
curves.
doi:10.1371/journal.pbio.1000129.g005
We confirm the correlation within participants between the
psychometric and neurometric curves over time by running a
bootstrap analysis on a per-participant basis. As expected, the slope
correlating the d-prime and neural response curves for each participant
yield a mean positive slope angle of 34.3u; bootstrap across participants
shows a mean of 32.7u, with the 5th to 95th confidence intervals falling
within the upper-right quadrant (Figure 5B).
We also note that the subsegments over which the neural
buildup is measured are required to span several rhythmic periods
(at least three; see Figure 6A). There is no buildup using intervals
with shorter durations, despite sufficient statistical power. (This
can be shown via the data plotted in the dashed curve in Figure 6A.
The normalized responses in the range 3.5 to 4.5 are elements of
an F(2,180) distribution, corresponding to p-values in the range
1.5% to 3.5%.) This implicates temporal phase coherence (in
contrast to spatial phase coherence) as critical to the buildup of the
neural target representation. That is, the power in each period is
not increasing, but the power integrated over several periods is
increasing. This can only occur if the phase variability decreases
with time, i.e., the neural buildup is due to a buildup in temporal
phase coherence rather than power.
similarity suggests that target detection is mediated by top-down
mechanisms analogous to those employed in auditory streaming
and object formation [50]. These streaming buildup effects tend to
operate over the course of a few seconds, and cannot be explained
by attentional buildup dynamics reported to be much faster or
much slower in time [51,52]. Moreover, the neural response to the
target rhythm also displays a statistically significant buildup
(Figure 5A, dashed red line) (bootstrap across participants,
p = 0.02) closely aligned with the behavioral curve, and consequently, decoupled from the actual acoustics. The remarkable
correspondence between these two measures strongly suggests that
the enhanced perception of the target over time is mediated by an
enhancement of the neural signal representation, itself driven by
an accumulation of sensory evidence mediated by top-down
mechanisms. No such neural buildup of the neural response to the
target rhythm is present for the masker task.
The MEG magnetic field distributions of the target rhythm
response component in Figure 5A (insets), showing the stereotypical pattern of neural activity originating separately in left and
right auditory cortex, illustrate the changing strength of the neural
activity over time in an individual participant.
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Figure 6. Analysis of neural buildup over time in target task for different duration windows, both for data and in a model of the
data. (A) Normalized neural response to target rhythm as a function of time, in target task, averaged over participants. The solid curve is identical to
the red curve in Figure 5. The dashed curve is the result of identical analysis except that the normalized neural response is calculated for every 250-ms
cycle of the target rhythm, rather than over the 750-ms window of three-cycles used above. Only the longer window shows buildup, implying that it
is not power per cycle that is growing, but phase coherence over several cycles. Error bars represent standard error over all participants. (B) Model
results for 750 ms (three cycles) windows, solid curve, and for 250 ms (one cycle) windows, dashed curve. The modeled normalized neural response
rises as temporal phase jitter decreases, but only in the three-cycle case, since the power per cycle is constant but the temporal phase coherence
across cycles increases. Error bars represent standard deviation over 30 simulation runs.
doi:10.1371/journal.pbio.1000129.g006
that temporal phase coherence (in contrast to spatial phase
coherence) is critical to the buildup of the neural target
representation.
This is further demonstrated by a quantitative model. Typical
simulated response profiles generated by the model are shown in
Figure 6B. The horizontal axis in the model is not increasing time,
but decreasing variability of the distribution of phase of the 4-Hz
signal (i.e., the phase of the signal has greater variability initially
As noted above, the subsegments, or windows, over which the
neural buildup is measured are required to span at least three
rhythmic periods, since there is no buildup observed using
intervals with shorter durations. Figure 6A illustrates this buildup
for both the three-cycle and one-cycle cases. The requirement of a
longer time window shows that the buildup is not merely due to
increased power at 4 Hz, since in that case, a window of one
rhythmic period would also show buildup. This in turn implies
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and gets more regular as one proceeds along the axis). In the threecycle window case, the buildup is pronounced, but not in the onecycle window case. Note that the model does not attempt to
emulate the downturn at the end of the experimental curve, nor
does it attempt to emulate the rate at which the buildup occurs as a
function of time (which would assume a linear decrease in
temporal phase coherence over time).
The model results show that buildup can be due to increasing
temporal coherence, and not due to increasing power. The neural
noise, representing the stochastic firing patterns of the neurons
underlying the MEG signal, is also required for the model’s
agreement with the data. A slight rise in the model’s one-cycle
window case may be seen, but it is not due to power (which never
changes), rather it is due to increased coherence over trials, which
is a weak side effect of increased temporal coherence.
Third, the data show a left-hemisphere bias in the cortical
representation of the target, for the target task, suggesting a
functional role of the left hemisphere in selective attention,
consistent with previous findings in visual [67] and auditory
[61,68] modalities. This bias may also be due to a left-hemisphere
bias specific to Heschl’s gyrus (the location of core auditory
cortex), for slow rhythmic tone pips (without a masker background), as seen in [69,70]. In contrast, for the masker task, the
hemispheric bias in cortical representation of the (now nonattended) target is reversed to the right, and might be simply due
to the nature of the attentional demands of the task (more diffuse
attention to the global structure of the sound), or to the righthemispheric bias of steady-state responses when attention is not
specifically directed to the rhythm [71]. It also appears, for both
tasks, that the deviant detection itself is not guiding the
lateralization of the response, running counter to that of Zatorre
and Belin [72], since the task/deviant requiring spectral change
detection shows a left-hemisphere bias, and the task/deviant
requiring temporal change detection shows a right-hemisphere
bias.
Finally, this study offers the first demonstration of the topdown–mediated buildup over time of the neural representation of
a target signal that also follows the same temporal profile of the
buildup based on listeners’ detectability performance in the same
participant. Using the current experimental paradigm, we are able
to monitor the evolution in time of attentional processes as they
interact with the sensory input. Many studies overlook the
temporal dynamics of the neural correlates of attention, either
by using cues that prime participants to the object of attention
(thereby stabilizing attention before the onset of the stimulus), or
by explicitly averaging out the buildup of the neural signal in their
data analysis (focusing instead on the overall contribution of
attention in different situations, and not monitoring the dynamics
by which the process builds up). Our findings reveal that even
though the sensory target signal is unchanged, attention allows its
neural representation to grow over time, closely following the time
course of the perceptual representation of the signal, within
participants.
Together, these findings support a view of a tightly coupled
interaction between the lower-level neural representation and the
higher-level cognitive representation of auditory objects, in a clear
demonstration of auditory scene segregation: the cocktail party
effect [73]. Our experimental paradigm allows both task-driven
(top-down) and stimulus-driven (bottom-up) processes to guide
perception. For listeners performing the target task, the target
rhythm is the attended auditory object, a foreground stream to be
separated from a noisy background. The masker task, requiring
the listener to reverse the role of the foreground and background,
allows the contrasting situation to be considered under otherwise
identical acoustical conditions. This permits a controlled deemphasis of the auditory role of the target rhythm, without the
need for a ‘‘passive’’ listening condition under which the amount
of the listener’s attention is lessened, but actually unknown, and
strongly variable across participants.
The data suggest that new models of attention may be required,
based on temporally coherent or locally synchronous neural
activity rather than neural amplification [74]. The buildup of
neural responses over time is seen only when integrated over
several periods of the target rhythm, but not for individual periods.
This result is difficult to explain using standard models of attention
that rely solely on gain-based changes, or even on gain/spectralsensitivity hybrid models [75,76]. Instead, a more plausible theory
of neural mechanisms underlying the role of top-down attention in
the buildup of perceptual streams would involve top-down
Discussion
This study’s novel experimental paradigm builds on previous
work in stream segregation using simpler stimuli [34,35,53,54], but
(1) using a richer stimulus design and (2) keeping the physical
parameters of the stimulus fixed while manipulating only the
attentional state of the listeners. One major finding is that auditory
attention strongly modulates the sustained (steady-state) neural
representation of the target. Specifically, sustained attention
correlates with a sustained increase in the time-varying neural
signal, in contrast with onset transients [22,55] or nonspecific,
constant (‘‘DC’’) [56–58] effects of attention on auditory signals.
The location of the modulated neural representation is consistent
with core auditory cortex, hence supporting current evidence
implicating neuronal mechanisms of core auditory cortex in the
analysis of auditory scenes [35,59–61]. Furthermore, this modulation of neural signal is significantly distant from the source of the
M100 and so cannot be explained as simply a train of repeated
M100 responses. This steady-state increase in the signal strength is
specific to the frequency of the target rhythm, and is additionally
complemented by an enhancement in coherence over distant
channels, reflecting an increased synchronization between distinct
underlying neural populations. This attentional effect (in both
power and phase) appears exclusively at the target frequency and
is absent not only from other frequency bands whose intrinsic
rhythms and induced response might show attentional changes,
but even from adjacent frequency bins, which argues against any
theory of neural recruitment or redistribution of energy at the lowfrequency spectrum. Therefore, our findings argue that processes
of attention interact with the physical parameters of the stimulus,
and can act exclusively to enhance particular features to be
attended to in the scene, with a resolution of a fraction of a hertz.
Our analysis focuses on steady-state components of feature-based
analysis, hence, complementing event-based analyses that relate
temporal components of the recorded potential to specific
mechanisms of feature-based attention [62–66].
Second, the data reveal that enhanced acoustic saliency
(driven by bottom-up processes), which causes an increase in
perceptual detectability, also correlates with an increase in the
sustained power and coherence of the neural signal. In this case,
the increase in neural signal occurs regardless of the task being
performed, but with different behavioral consequences: in the
target task, it leads to an increase in performance, but in the
masker task, a decrease (via interference). This outcome allows
us to give different explanations of this ‘‘attentionally modulated’’ neural change: as a marker of object detectability during the
first task, but as a neural correlate of perceptual interference
during the second task.
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Attention in Auditory Scenes Analysis
per stimulus); and combined condition (one target deviant and one
masker deviant, at independent times, per stimulus). Each target
deviant was the displacement of a target note (upward or
downward) by two semitones from the target frequency. Each
masker deviant was a single 500-ms time window in which all
masker tones were elongated from 75 ms to 400 ms. The temporal
location of the deviant (for both target and masker tasks) was
randomly distributed along the 5.5-s trial duration, with timing as
indicated by the behavioral buildup curve in Figure 4.
projections acting in conjunction with the physical stimulus as
regulators or clocks for the firing patterns of neuronal populations
in auditory cortex. Another conceivable mechanism for this
increase in temporal coherence may arise from general sharpening
of temporal tuning, which would work for auditory streams far
more complex than the regular stream presented here. The neural
underpinnings of this bottom-up/top-down interaction are likely
to mediate changes in the response profiles of cortical neurons, via
mechanisms of synaptic and receptive field plasticity which have
been shown to be gated by attention; whereby attention plays a
crucial role in shifting cortical circuits from one state to another
depending on behavioral demands [77–80]. We speculate that
temporal patterns of neuronal firings are crucial in any scene
segregation task to resolve the competition between attended and
unattended objects, hence, delimiting the cognitive border
between different streams.
Overall, a significant outcome of this study is that it not only
demonstrates a strong coupling between the measured neural
representation of a signal and its perceptual manifestation, but also
places the source of this coupling at the level of sensory cortex. As
such, the neural representation of the percept is encoded using the
feature-driven mechanisms of sensory cortex, but shaped in a
sustained manner via attention-driven projections from higherlevel areas. Such a framework may underlie general mechanisms
of ‘‘scene’’ organization in any sensory modality.
Experimental Procedure
In the psychoacoustic experiment, participants were presented
with 180 stimuli (three protection regions6four conditions615
exemplars) per task. The progression from one trial to the next was
initiated by the participant with a button-press.
In the MEG experiment, only the eight-semitone spectral
protection region half-width was used, giving 60 stimuli (1
protection region 6 4 conditions 6 15 exemplars) per task. The
interstimulus intervals (ISIs) were randomly chosen to be 1,800,
1,900, or 2,000 ms. For each task, the participants were presented
with three blocks, repeating the ensemble of 60 stimuli three times
(totaling 180 stimuli). Participants were allowed to rest after each
block, but were required to stay still.
The identical stimulus ensemble (including identical ISIs in the
MEG case) was presented for both target and masker tasks.
Depending on the task being performed, participants were
instructed to listen for the presence of a frequency deviant in the
target rhythm (target task) or a duration deviant in the masker
(masker task); each task deviant was present in exactly half the
trials.
Psychoacoustical study. Participants were seated at a
computer in a soundproof room. The signals were created
offline and presented diotically through Sony MDR-V700
headphones. Participants controlled the computer using a
Graphical User Interface (GUI) using the mouse. The task, as
well as the basic use of the GUI, was described to participants.
Participants were allowed to adjust the volume to a comfortable
level before proceeding with the experiment.
A training block of 20 trials was presented before each task. In
the target task training, the protection region half-width decreased
from 12 semitones to four semitones in steps of four semitones. In
the masker task training, it increased from four semitones to 12
semitones in steps of four semitones. Participants were permitted
to listen to each sound as many times as desired; then participants
were prompted to indicate whether a deviant was present. The
correct answer was displayed afterwards. Participants pressed a
button to initiate the presentation of the next stimulus.
Each participant performed both the masker task and the target
task, with task order counterbalanced across participants. Each
task required the participant to listen to the entire set of 180
stimuli described above. Each stimulus was presented only once,
and no feedback was given after each trial. The entire session of
both tasks lasted approximately 1.5 h.
MEG study. Participants were placed horizontally in a dimly
lit magnetically shielded room (Yokogawa Electric Corporation).
Stimuli
were
presented
using
Presentation
software
(Neurobehavioral Systems). The signals were delivered to the
participants’ ears with 50-V sound tubing (E-A-RTONE 3A;
Etymotic Research), attached to E-A-RLINK foam plugs inserted
into the ear canal, and presented at a comfortable loudness of
approximately 70 dB SPL. The entire acoustic delivery system is
equalized to give an approximately flat transfer function from 40–
3,000 Hz.
Materials and Methods
Participants
Nine participants (six males; mean age 29 y, range 24–38 y)
participated in the psychoacoustic study. Eighteen participants (11
males; mean age 27 y, range 21–49 y) participated in the MEG
study. Three participants took part in both studies. Among the 18
participants in the MEG study, four participants were excluded
from further analysis due to an excess of nonneural electrical
artifacts or an inability to perform the tasks, leaving 14 participants
(eight males; mean age 27 y, range 21–49 y). All participants were
right handed [81], had normal hearing, and had no history of
neurological disorder. The experiments were approved by the
University of Maryland Institutional Review Board, and written
informed consent was obtained from each participant. Participants
were paid for their participation.
Stimulus Design
The stimuli were generated using MATLAB (MathWorks).
Each trial was 5.5 s in duration with 8-kHz sampling. Every trial
contained one target note, repeating at 4 Hz, whose frequency was
randomly chosen in the range 250–500 Hz in two semitone
intervals. The background consisted of random tones at a density
of 50 tones/s, uniformly distributed over time and log-frequency
(except for the spectral protection region). The frequencies of the
random notes were randomly chosen from the five-octave range
centered at 353 Hz, in two semitone intervals, with the constraint
that no masker components were permitted within a four, or eight,
or 12 semitone around the target frequency (the spectral
protection region half-width). This random sampling of masker
frequencies ensures a minimum spectral distance of two semitones
between maskers, and keeps the probability of harmonically
related maskers minimal. Masker and target tones were 75 ms in
duration with 10-ms onset and offset cosine ramps. All masker
tones were presented at the same intensity as the target tone.
Fifteen exemplar stimuli were generated for each of the four
condition types: null condition (no deviants); target condition (one
target deviant per stimulus); masker condition (one masker deviant
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Attention in Auditory Scenes Analysis
high false alarm rate and low hit rate, and were excluded from
the analysis of buildup.
Neural data analysis. After recordings were completed and
noise reduction algorithms applied, the responses to each stimulus,
from 1.25 s to 5.5 s poststimulus, were extracted and
concatenated, forming a single extended response with duration
T = 765 s (4.25 s660 sounds6three blocks) for each channel. This
was done separately for each task block. The discrete Fourier
transform (DFT) was applied on the single response, giving a single
Fourier response from 0 to 500 Hz with frequency resolution of 1/
765 Hz.
The evoked neural responses to the target sequences were
characterized by the magnitude and phase of the frequency
component at 4 Hz (the tone presentation rate) and were used
for localization and for phasor maps. The complex magnetic
field strength is given by the product of the value of DFT times
the sampling interval (1/fs), and has units of fT/Hz. Power
spectral density is calculated as the product of the inverse
duration (1/T) times the modulus squared of the complex
magnetic field strength, and has units of fT2/Hz. The remainder
of the analysis was based on the normalized neural responses,
defined to be the squared magnitude of the frequency
component at 4 Hz divided by the average squared magnitude
of the frequency components between 3 Hz and 5 Hz (excluding
the component at 4 Hz), averaged over the 20 channels with the
strongest normalized neural responses for each participant. The
channels were allowed to vary from participant to participant to
allow for inter-participant configuration variability. Using 10,
20, or 50 channels yielded similar findings; however, only the 20
channel analysis is reported here, indicating that this method is
robust against the particular subset of channels used. This
normalization is not biased by the task, since the average
squared magnitude of the frequency components between 3 Hz
and 5 Hz did not significantly differ between tasks.
The spatial pattern of the neural responses was represented by a
phasor map, a graph of the complex (magnitude and phase)
magnetic field on all channels. For each channel, the length of the
vector arrow is proportional to the magnitude of the 4-Hz
frequency component, and the direction of the arrow represents
the phase according to standard polar coordinates. Red and green
contours represent the magnetic field strength projected onto the
line of constant phase that maximizes the projected field’s variance
[43]. The phasors are visually faded using the signal-to-noise ratio
(SNR) of each channel as linear fading coefficients.
The normalized neural responses difference between target task
and masker task was averaged across 14 participants to
characterize attention gain effect. Furthermore, to evaluate the
effect of attention at across frequencies, the same analysis is done
at 4 Hz and the two adjacent frequency bins (4 Hz2Df and
4 Hz+Df ), and also at 11 frequencies in the alpha, theta, and low
gamma frequency bands, in approximately 5-Hz increments from
approximately 5 Hz to approximately 55 Hz. For consistency with
the frequencies examined in tests of phase coherence (next), we
used Df = 1/(4.25) Hz, with analysis performed only at its integer
multiples (e.g., 17Df = 4.0 Hz and 21Df<4.94 Hz). The normalization is only weakly affected by the task, since the average
squared magnitude of the frequency components did not vary
strongly by task (no difference below 5 Hz, a relative enhancement
in the target task of approximately 1 dB from 5 to 25 Hz, and a
relative enhancement for the masker task of approximately 1 dB
from 25 to 50 Hz).
To study attention modulation effects on the synchronization
between two distinct neural populations, phase coherence between
channels m and n, c2mn, is obtained from Q = 180 trials [84,85]:
Before the main experiment, a pre-experiment was run in which
a 1-kHz, 50-ms tone pip was presented about 200 times. The ISI
was randomized between 750 ms and 1,550 ms, and participants
were instructed to count the tone pips. The aim of this task was to
record the M100 response (a prominent peak approximately
100 ms after pip onset, also called N1m) used for differential
source localization. The responses were checked to verify that the
location and strength of neural signals fell within a normal range.
In the main experiment, participants were presented with three
blocks of the 60 stimuli described above. Each participant
performed both the masker task and the target task, with task
order counterbalanced across participants. Participants were
instructed to press a button held in the right hand as soon as
they heard the appropriate deviant.
A training block with 12 sounds was presented before each task.
Each training sound was presented twice. Participants verbally
indicated the existence of the deviants and the feedback was given
by the investigator. The entire session of both tasks lasted
approximately 1 h.
MEG recordings were conducted using a 160-channel wholehead system (Kanazawa Institute of Technology). Its detection
coils are arranged in a uniform array on a helmet-shaped surface
on the bottom of the dewar, with about 25 mm between the
centers of two adjacent 15.5-mm-diameter coils. Sensors are
configured as first-order axial gradiometers with a baseline of
50 mm; their field sensitivities are 5 fT/!Hz or better in the white
noise region. Three of the 160 channels are magnetometers
separated from the others and used as reference channels in noisefiltering methods. The magnetic signals were bandpassed between
1 Hz and 200 Hz, notch filtered at 60 Hz, and sampled at the rate
of fs = 1,000 Hz. All neural channels were denoised twice with a
block least mean square (LMS) adaptive filter, first, using the three
external reference channels [41], and second, using the two
channels with the strongest cardiac artifacts [82].
Data Analysis
Behavioral performance analysis. The ability of
participants to perform the requested task was assessed by
calculating a d-prime measure of performance [83]. For each
condition (i.e., each task and protection region), we estimated the
correct detection and false alarm probabilities for detecting the
target or masker deviants; converted them to normal deviates (zscores), and computed the d-prime value. The performance shown
in Figure 1 depicts the mean d-prime values across participants.
The error bars represent the standard error of mean.
To determine the effect of the target’s tonal frequency on the
neural responses, the stimuli were divided spectrally: each sound
was characterized as a low- or high-frequency target tone sequence
depending on the target tone’s relation to the middle frequency
353 Hz (those with target tone frequency of 353 Hz were
randomly assigned as low or high in such a way as to equipartition
the high and low categories). A d-prime measure was then derived
for each of the low or high target trials from both target and
masker tasks.
To investigate the buildup of the target object during the
target task, we divided the deviant trials according to the nine
possible temporal locations of the deviant throughout the
stimulus sequence. A probability of hit was then measured for
each trial. Because of the temporal uncertainty in the false alarm
trials, we calculated an average false alarm rate (irrespective of
when the false response was issued), and combined it with the
time-specific hit rate to derive a d-prime measure for each time
segment. Using this behavioral assessment measure, five
participants yielded nonpositive d-prime values due to their
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Attention in Auditory Scenes Analysis
c2mn ð f Þ~
2
Xmn ð f Þ SXmm ð f ÞTSXnn ð f ÞT
from 1.25 s to 2 s poststimulus (or 2 s to 2.75 s, etc.), were
extracted and concatenated, forming a single extended response
with duration T = 135 s (0.75 s 6 60 sounds 6 3 blocks) for each
channel. The discrete Fourier transform (DFT) was applied on the
single response, giving a single Fourier response of from 0 to
500 Hz with frequency resolution 1/135 Hz. The first segment
began at 1,250-ms poststimulus since earlier time intervals showed
substantial power at the frequency corresponding to the segment
duration, an artifact indicating that the measured spectral power
was extrinsic to the analysis window, not intrinsic to the neural
signal. The segment duration of 750 ms was used since shorter
durations did not show the buildup effect, an effect that is
elaborated upon in the discussion of the quantitative model of
neural buildup. An analogous analysis of phase coherence buildup
over time was performed but did not yield significant results.
,
where Xmn( f ) is the average cross spectrum between channel m
and channel n, Xmm( f ) is average power spectrum of the individual
channel m:
Xmn ð f Þ~ Q1
Q
P
Fmq ð f ÞFnq ð f Þ ,
q~1
where Fmq ð f Þ is the Fourier transform of the qth trial of channel m
at frequency f. A coherence value of one indicates that the two
channels maintain the same phase difference on every trial,
whereas a coherence value near zero indicates a random phase
difference across trials. The coherence difference between target
task and masker task was computed for every channel pair. The
standard error of the mean (SEM) emn was constructed to identify
robust coherence change [84,85]:
Behavioral versus neural correlation and bootstrap
analysis. We correlated the effect of high versus low target
frequencies in the behavioral and neural responses by contrasting
the per-participant psychometric and neurometric measures. First,
we scaled the neural data (i.e., the normalized responses to target)
by a factor of three in order to match the absolute ranges of both
neural and behavioral values. We then derived the angle (i.e.,
inverse tangent) of the slope relating the high versus low
frequencies of the behavioral and neural data points for each
participant and each task. The across-participant slopes were then
combined using circular statistics to yield an angular mean for
each task [86].
We performed a bootstrap procedure in order to confirm the
positive (respectively, negative) correlation between the neurometric and psychometric functions in the target (respectively,
masker) task. We followed a balanced bootstrap sampling
procedure [87] by randomly selecting 14 participants with
replacement and computing their angular sample mean and
repeating this process 1,000 times. The procedure was controlled
to ensure that all participants appeared the same number of times
over all 1,000 bootstrap samplings. Confidence measures were
then derived from the bootstrap statistics.
A similar statistical analysis was performed to correlate the
psychometric and neurometric curves for the target detection
buildup. To match the range of values from the neural and
behavioral data, we scaled the neural responses by a factor of two
(note that the different scaling is due to the reduced values of the
normalized neural response due to the smaller window for the
buildup analysis). The behavioral curves for each participant were
then interpolated to match the sampling rate of the neural data.
Subsequently, these two curves were then fitted by a first-order
polynomial to derive the slope relating the two functions. The
slope value was transformed into an angle and then combined
across participants, following the same procedure described above.
Note that the five participants with negative d-prime values were
excluded from this correlation analysis, because of their questionable behavioral performance.
Neural source localization. Source localization for the
M100 response was obtained by calculating the currentequivalent dipole best fitting the magnetic field configuration at
the M100 peak, in each hemisphere. Source localization for the
neural response to the target was obtained by calculating the
complex current-equivalent dipole best fitting the complex
magnetic field configuration at 4-Hz peak, in each hemisphere
[43]. Only channels with SNR .4 were used in the fitting.
Goodness of fit, as a function of the complex current-equivalent
dipole, is given by one minus the residual variance ratio. As
discussed in [43], the goodness of fit for complex magnetic field
distributions and complex dipoles gives typical values that are
sffiffiffiffi
2 1{c2 mn
emn ~
:
Q jcmn j
To emphasis phase modulation in auditory cortex, each
participant’s 20 channels with the strongest normalized neural
response at target rate were included in further analysis. In
addition, to exclude the artificial coherence that resulted from
volume conduction effects on extracranial magnetic field and
measure genuine phase correlation between distinct populations of
neurons, only long-distance channel pairs (channel separation
.100 mm) were included [85]. The difference between number of
channel pairs with robust increased coherence and channel pairs
with decreased coherence is normalized over the total number of
long-range channel pairs for each participant. Furthermore, to
evaluate the effect of attention at across frequencies, the same
analysis is done at 4 Hz and the two adjacent frequency bins
(4 Hz2Df and 4 Hz+Df ), and also at 11 frequencies in the alpha,
theta, and low gamma frequency bands, in approximately 5 Hz
increments from approximately 5 Hz to approximately 55 Hz. For
phase coherence (measured across trials), Df = 1/(4.25) Hz, and
phase coherence was analyzed only at its integer multiples (e.g.,
17Df = 4.0 Hz and 21Df<4.94 Hz).
To investigate the possibility of hemispheric bias, the 20
channels with the strongest normalized neural response at the
target rate were chosen from left and right hemispheres,
respectively, to represent the overall neural activity of each
hemisphere. Neural responses averaged across the 20 channels
were subtracted across hemispheres for each task and for all 14
participants. Using 10, 20, or 60 channels yielded similar findings;
however, only the 20-channel analysis is reported here.
To determine the effect of the tonal frequency on the neural
responses, the stimuli were divided spectrally as described above.
The neural responses at the target rate (both normalized response
and phase coherence), from low- and high-frequency target tone
stimuli, were obtained for each participant in the same way as
described above, but with only appropriate epochs concatenated
or phase-averaged.
To investigate the buildup of the target object in target task, the
responses were divided temporally: the analysis epochs were
divided into five temporal segments of 750-ms duration each, e.g.,
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Attention in Auditory Scenes Analysis
different distributions of the phase random variable with standard
deviations ranging from 1/60 to 1/12 of a cycle.
The model’s level of Gaussian white noise was obtained by the
biological requirement that the model’s normalized response for
the three-cycle window, with highest jitter, match the data’s; the
buildup rate as a function of decreased variability, as well as the
average level of the normalized response for the one-cycle window,
follow automatically. The simulated response results depend
weakly on the number of channels simulated. The experimental
MEG system records from 157 channels, but not all are strongly
auditory. The results shown here use 100 simulated auditory
channels.
much lower than for comparable real distributions, due to the
doubling of number of degrees of freedom absorbing noise power.
Significance of the relative displacement between the M100 and 4Hz dipole sources were determined by a two-tailed paired t-test in
each of three dimensions: lateral/medial, anterior/posterior, and
superior/inferior.
Quantitative Model of Neural Buildup
A model simulation to illustrate a mechanism of neural buildup
was implemented in MATLAB (MathWorks). The model
simulates MEG responses by generating 4-Hz signals whose phase
is a random variable with constant mean, additionally corrupted
by additive Gaussian white noise. This noise represents neural
variability inherent in the neural processing mechanisms underlying the MEG signal and is critical to the model (external
magnetic field noise had already been removed from the data by
active filtering [41] and is not modeled). The normalized response
power was calculated by the same method as in the experiment:
concatenating 50 signals and normalizing the power at 4 Hz by
the average power in the 3–5 Hz band, averaging the 20 best
channels (out of 100 simulated auditory channels), and then
averaging that over simulation runs. This was done for five
Acknowledgments
We thank Jonathan Fritz and David Poeppel for comments and discussion.
We are grateful to Jeff Walker for excellent technical support.
Author Contributions
The author(s) have made the following declarations about their
contributions: Conceived and designed the experiments: ME JX SAS
JZS. Performed the experiments: ME JX SAS JZS. Analyzed the data: ME
JX SAS JZS. Wrote the paper: ME JX SAS JZS.
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